In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the policy is continually updated and the induced distribution over visited states used for model learning shifts accordingly. Prior methods alleviate this issue by quantifying the uncertainty of model-generated samples. However, these methods only quantify the uncertainty passively after the samples were generated, rather than foreseeing the uncertainty before model trajectories fall into those highly uncertain regions. The resulting low-quality samples can induce unstable learning targets and hinder the optimization of the policy. Moreover, while being learned to minimize one-step prediction errors, the model is generally used to predict for multiple steps, leading to a mismatch between the objectives of model learning and model usage.

A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical -- without much consideration for the human end-user. In particular, it is not yet clear (1) how useful current explainability methods are in real-world scenarios; and (2) whether current performance metrics accurately reflect the usefulness of explanation methods for the end user. To fill this gap, we conducted psychophysics experiments at scale ($n=1,150$) to evaluate the usefulness of representative attribution methods in three real-world scenarios. Our results demonstrate that the degree to which individual attribution methods help human participants better understand an AI system varies widely across these scenarios. This suggests the need to move beyond quantitative improvements of current attribution methods, towards the development of complementary approaches that provide qualitatively different sources of information to human end-users.

The paper presents a novel method for predicting gene regulation by LSTM with an attention mechanism. The model consists of two levels, where the first level is applied on bins for each histone modifications (HM) and the second level is applied to multiple HMs. Attention mechanism is used in each level to focus on the important parts of the bins and HMs. In the experiments, the proposed method improves AUC scores over baseline models including CNN, LSTM, and CNN with an attention mechanism. This is an interesting paper which shows that LSTM with an attention mechanism can predict gene regulation.

From powering airplanes to generating electricity to cooking and much more, the world depends on a great deal of its energy in the form of "Fuel". The price of fuel fluctuates with revisions in crude oil prices or other global events and is also reflective of the political and economic state of a country. Predicting fuel prices remains a major bottleneck. So the question is: can artificial intelligence predict the fuel price? The answer is a big yes.

The AI tools in the experiment would be used to gather real-time analysis of data from sensors across the globe. Information would also be collected from'commercially available information', sourced from unnamed partners. VanHerck went on to say how allies and other partners could also have access to the information in real-time as it's possible to share it via cloud-based systems.

The future of work will depend highly on soft skills. No matter how AI for recruitment and talent assessment is leveraged in the future, a candidate's high-order thinking and EQ will stay vital, something which the robots simply can't replace or automate! This accurate AI-powered tool (beyond IBM Watson) gives you full picture of a candidate's soft skill background (based on the Big 5 personality test, DISC OCEAN, mood graphs, sentiment analysis, digital footprint analysis, behavior score, and much more) to help recruiters spot and process the right'candidates' who would add to their diverse, inclusive company culture. Get a free assessment report, at: https://frrole.ai/deepsense-app/ You just need the twitter handle/ email ID of the individual to get started.

Google's AI researchers recently showed off a new method for teaching computers to understand why some images are more aesthetically pleasing than others. Traditionally, machines sort images using basic categorization – like determining whether an image does or does not contain a cat. The new research demonstrates that AI can now rate image quality, regardless of category. The process, called neural image assessment (NIMA), uses deep learning to train a convolutional neural network (CNN) to predict ratings for images. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network … Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline.

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.

Humans perform visual recognition/detection and then predict motion of independent objects. The brain relies strongly on understanding of physical world and object geometry/affordances in estimating motion (and thus it's much more difficult), the "one model fits all" approach is completely wrong. Shouldn't the baseline be detection - motion estimation? If you are really interesting in solving the robotic arm problem why not just create a network that leverages ground truth information about location and motion of the arm.